where \(\textbf {F}_{i} \in \mathbb {R}^{(\widetilde{H}/2^{i+\mathbb {1}(i=1)})\times (\widetilde{W}/2^{i+\mathbb {1}(i=1)})\times D_{i}}\)  denotes the feature map generated by the \(i\) -th stage of the backbone network \(\mathcal {B}\)  with the pre-trained weights of \(\Theta _{b}\) ; \(L\)  is the number of multi-scale features; \(\mathbb {1}\)  means the indicator function. \(D_{i}\)  is the dimension of the feature. In the framework, we adopt the frequently-used backbone network ResNet50 [1]} like most existing scene text detection models.
